Machine Learning Engineer, Applied Research

About the role

Whatnot is the largest live shopping platform in North America and Europe, enabling users to buy, sell, and discover items ranging from trading cards and fashion to electronics and live plants. Sellers build real businesses across hundreds of categories, and the company is pioneering live commerce at unprecedented scale in the West. As a remote‑co‑located team with hubs in the US, UK, Ireland, Poland, Germany, and Australia, Whatnot moves fast, stays close to users, and focuses on high‑impact work. The team was recently named the #1 Best Startup Employer in America by Forbes.

• Lead research projects across the marketplace dynamics problem area: simulation, auction and allocation mechanics, long‑term objective modeling, exploration and information value, or marketplace experimentation methods.
• Take ideas from hypothesis to production: literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller org.
• Build models of how the marketplace behaves as a system: learned simulators that predict segment‑level effects of ranking and policy changes, and surrogate models of long‑term marketplace outcomes.
• Model Whatnot's actual market mechanics: auction and bidding dynamics, and discovery exposure allocation as a portfolio problem, including allocation to rising sellers.
• Advance how a multi‑sided live marketplace evaluates changes: off‑policy evaluation, switchback and interference‑robust experiment designs, and variance reduction.
• Contribute to Whatnot's external technical presence through publications, open‑source work, and public benchmarks.
• Team members in this role are required to be within commuting distance (50 miles) of the New York City hub.

Requirements

• 5+ years of industry experience building and deploying ML models to solve user problems at scale.
• Depth in at least one of: recommendation systems, causal inference, off‑policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation.
• A track record of applying scientific methods to solve real‑world problems on consumer‑scale data.
• Advanced proficiency in Python, SQL, and common ML frameworks like PyTorch, XGBoost, etc.
• Strong grounding in applied statistics, experiment design, and theoretical machine learning.
• Strong communication and leadership skills; ability to influence roadmaps and align cross‑functional teams in a remote environment.

Qualifications

• Experience in two‑sided marketplaces, ads and auction systems, or pricing.
• Experience building simulators or economic models of platform behavior.

Benefits

• Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
• Health Insurance options including Medical, Dental, Vision
• Work From Home Support
• Home office setup allowance
• Monthly allowance for cell phone and internet
• Care benefits
• Monthly allowance for wellness
• Annual allowance towards Childcare
• Lifetime benefit for family planning, such as adoption or fertility expenses
• Retirement: 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
• Monthly allowance to dogfood the app
• Parental Leave: 16 weeks of paid parental leave + one month gradual return to work (company leave allowances run concurrently with country leave requirements)

Pay

Salary range: $210,000 – $300,000 per year (base salary) + benefits + equity.